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> ### > attach(NULL, name = "CheckExEnv") > assign(".CheckExEnv", as.environment(2), pos = length(search())) # base > ## add some hooks to label plot pages for base and grid graphics > setHook("plot.new", ".newplot.hook") > setHook("persp", ".newplot.hook") > setHook("grid.newpage", ".gridplot.hook") > > assign("cleanEx", + function(env = .GlobalEnv) { + rm(list = ls(envir = env, all.names = TRUE), envir = env) + RNGkind("default", "default") + set.seed(1) + options(warn = 1) + delayedAssign("T", stop("T used instead of TRUE"), + assign.env = .CheckExEnv) + delayedAssign("F", stop("F used instead of FALSE"), + assign.env = .CheckExEnv) + sch <- search() + newitems <- sch[! sch %in% .oldSearch] + for(item in rev(newitems)) + eval(substitute(detach(item), list(item=item))) + missitems <- .oldSearch[! .oldSearch %in% sch] + if(length(missitems)) + warning("items ", paste(missitems, collapse=", "), + " have been removed from the search path") + }, + env = .CheckExEnv) > assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now > assign("ptime", proc.time(), env = .CheckExEnv) > grDevices::postscript("msm-Examples.ps") > assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv) > options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly")) > options(warn = 1) > library('msm') > > assign(".oldSearch", search(), env = .CheckExEnv) > assign(".oldNS", loadedNamespaces(), env = .CheckExEnv) > cleanEx(); ..nameEx <- "crudeinits.msm" > > ### * crudeinits.msm > > flush(stderr()); flush(stdout()) > > ### Name: crudeinits.msm > ### Title: Calculate crude initial values for transition intensities > ### Aliases: crudeinits.msm > > > ### ** Examples > > data(heart) > twoway4.q <- rbind(c(-0.5, 0.25, 0, 0.25), c(0.166, -0.498, 0.166, 0.166), + c(0, 0.25, -0.5, 0.25), c(0, 0, 0, 0)) > statetable.msm(state, PTNUM, data=heart) to from 1 2 3 4 1 1367 204 44 148 2 46 134 54 48 3 4 13 107 55 > crudeinits.msm(state ~ years, PTNUM, data=heart, qmatrix=twoway4.q) 1 2 3 4 1 -0.1173149 0.06798932 0.0000000 0.04932559 2 0.1168179 -0.37584883 0.1371340 0.12189692 3 0.0000000 0.04908401 -0.2567471 0.20766310 4 0.0000000 0.00000000 0.0000000 0.00000000 > > > > cleanEx(); ..nameEx <- "deltamethod" > > ### * deltamethod > > flush(stderr()); flush(stdout()) > > ### Name: deltamethod > ### Title: The delta method > ### Aliases: deltamethod > ### Keywords: math > > ### ** Examples > > > ## Simple linear regression, E(y) = alpha + beta x > x <- 1:100 > y <- rnorm(100, 4*x, 5) > toy.lm <- lm(y ~ x) > estmean <- coef(toy.lm) > estvar <- summary(toy.lm)$cov.unscaled > > ## Estimate of (1 / (alphahat + betahat)) > 1 / (estmean[1] + estmean[2]) (Intercept) 0.2147732 > ## Approximate standard error > deltamethod (~ 1 / (x1 + x2), estmean, estvar) [1] 0.009156747 > > > > cleanEx(); ..nameEx <- "medists" > > ### * medists > > flush(stderr()); flush(stdout()) > > ### Name: medists > ### Title: Measurement error distributions > ### Aliases: medists dmenorm pmenorm qmenorm rmenorm dmeunif pmeunif > ### qmeunif rmeunif > > > ### ** Examples > > ## what does the distribution look like? > x <- seq(50, 90, by=1) > plot(x, dnorm(x, 70, 10), type="l", ylim=c(0,0.06)) ## standard Normal > lines(x, dtnorm(x, 70, 10, 60, 80), type="l") ## truncated Normal > ## truncated Normal with small measurement error > lines(x, dmenorm(x, 70, 10, 60, 80, sderr=3), type="l") > > > > cleanEx(); ..nameEx <- "msm" > > ### * msm > > flush(stderr()); flush(stdout()) > > ### Name: msm > ### Title: Multi-state Markov and hidden Markov models in continuous time > ### Aliases: msm > ### Keywords: models > > ### ** Examples > > ### Heart transplant data > ### For further details and background to this example, see > ### the PDF manual in the doc directory. > data(heart) > print(heart[1:10,]) PTNUM age years dage sex pdiag cumrej state 1 100002 52.49589 0.000000 21 0 IHD 0 1 2 100002 53.49863 1.002740 21 0 IHD 2 1 3 100002 54.49863 2.002740 21 0 IHD 2 2 4 100002 55.58904 3.093151 21 0 IHD 2 2 5 100002 56.49589 4.000000 21 0 IHD 3 2 6 100002 57.49315 4.997260 21 0 IHD 3 3 7 100002 58.35068 5.854795 21 0 IHD 3 4 8 100003 29.50685 0.000000 17 0 IHD 0 1 9 100003 30.69589 1.189041 17 0 IHD 1 1 10 100003 31.51507 2.008219 17 0 IHD 1 3 > twoway4.q <- rbind(c(-0.5, 0.25, 0, 0.25), c(0.166, -0.498, 0.166, 0.166), + c(0, 0.25, -0.5, 0.25), c(0, 0, 0, 0)) > statetable.msm(state, PTNUM, data=heart) to from 1 2 3 4 1 1367 204 44 148 2 46 134 54 48 3 4 13 107 55 > crudeinits.msm(state ~ years, PTNUM, data=heart, qmatrix=twoway4.q) 1 2 3 4 1 -0.1173149 0.06798932 0.0000000 0.04932559 2 0.1168179 -0.37584883 0.1371340 0.12189692 3 0.0000000 0.04908401 -0.2567471 0.20766310 4 0.0000000 0.00000000 0.0000000 0.00000000 > heart.msm <- msm( state ~ years, subject=PTNUM, data = heart, + qmatrix = twoway4.q, death = 4, + control = list ( trace = 2, REPORT = 1 ) ) Nelder-Mead direct search function minimizer function value for initial parameters = 4908.816768 Scaled convergence tolerance is 7.31471e-05 Stepsize computed as 0.179577 BUILD 8 5123.815245 4884.143455 EXTENSION 10 5022.293143 4636.885160 LO-REDUCTION 12 4929.762879 4636.885160 LO-REDUCTION 14 4917.244299 4636.885160 LO-REDUCTION 16 4915.322532 4636.885160 LO-REDUCTION 18 4908.816768 4636.885160 EXTENSION 20 4899.205981 4475.549989 LO-REDUCTION 22 4884.143455 4475.549989 EXTENSION 24 4741.752609 4292.599737 LO-REDUCTION 26 4731.148839 4292.599737 LO-REDUCTION 28 4706.313298 4292.599737 LO-REDUCTION 30 4653.900890 4292.599737 EXTENSION 32 4636.885160 4186.424294 EXTENSION 34 4503.780841 4059.497277 LO-REDUCTION 36 4475.549989 4059.497277 LO-REDUCTION 38 4404.832092 4059.497277 LO-REDUCTION 40 4374.721255 4059.497277 REFLECTION 42 4300.407666 4057.951149 REFLECTION 44 4292.599737 4054.496131 LO-REDUCTION 46 4186.424294 4054.496131 REFLECTION 48 4137.152610 4036.693114 LO-REDUCTION 50 4108.008169 4036.693114 LO-REDUCTION 52 4091.776646 4036.693114 LO-REDUCTION 54 4063.155599 4036.693114 EXTENSION 56 4059.497277 4025.529798 HI-REDUCTION 58 4057.951149 4025.529798 LO-REDUCTION 60 4054.496131 4025.529798 LO-REDUCTION 62 4045.457072 4025.529798 LO-REDUCTION 64 4043.815113 4025.529798 REFLECTION 66 4041.436285 4024.560509 REFLECTION 68 4041.084664 4021.694515 LO-REDUCTION 70 4036.693114 4021.694515 LO-REDUCTION 72 4033.418571 4021.694515 LO-REDUCTION 74 4030.788752 4021.694515 LO-REDUCTION 76 4030.685582 4021.694515 REFLECTION 78 4029.096902 4021.207435 LO-REDUCTION 80 4025.650397 4021.207435 LO-REDUCTION 82 4025.529798 4021.207435 LO-REDUCTION 84 4025.445399 4021.207435 EXTENSION 86 4024.560509 4017.588685 LO-REDUCTION 88 4024.303669 4017.588685 LO-REDUCTION 90 4023.920002 4017.588685 EXTENSION 92 4021.882363 4016.265925 EXTENSION 94 4021.737283 4014.262284 EXTENSION 96 4021.694515 4012.067553 LO-REDUCTION 98 4021.207435 4012.067553 LO-REDUCTION 100 4020.437778 4012.067553 EXTENSION 102 4019.170173 4008.235094 LO-REDUCTION 104 4017.588685 4008.235094 EXTENSION 106 4016.265925 4006.526304 EXTENSION 108 4014.394295 4005.744955 EXTENSION 110 4014.262284 4000.449437 LO-REDUCTION 112 4013.628557 4000.449437 LO-REDUCTION 114 4012.067553 4000.449437 LO-REDUCTION 116 4011.239977 4000.449437 LO-REDUCTION 118 4008.235094 4000.449437 LO-REDUCTION 120 4006.526304 4000.449437 LO-REDUCTION 122 4005.744955 4000.449437 REFLECTION 124 4005.255301 4000.280656 REFLECTION 126 4003.483979 3998.886017 LO-REDUCTION 128 4001.794657 3998.886017 LO-REDUCTION 130 4001.766045 3998.886017 EXTENSION 132 4001.442109 3997.375449 LO-REDUCTION 134 4001.388662 3997.375449 EXTENSION 136 4000.449437 3992.597986 LO-REDUCTION 138 4000.280656 3992.597986 LO-REDUCTION 140 3999.898537 3992.597986 LO-REDUCTION 142 3999.605848 3992.597986 LO-REDUCTION 144 3998.886017 3992.597986 LO-REDUCTION 146 3997.553881 3992.597986 EXTENSION 148 3997.375449 3989.875844 LO-REDUCTION 150 3996.854598 3989.875844 EXTENSION 152 3996.810247 3988.028376 EXTENSION 154 3993.577608 3981.087107 LO-REDUCTION 156 3993.443339 3981.087107 LO-REDUCTION 158 3993.073390 3981.087107 EXTENSION 160 3992.597986 3977.141676 LO-REDUCTION 162 3990.667213 3977.141676 LO-REDUCTION 164 3989.875844 3977.141676 EXTENSION 166 3988.028376 3972.778681 LO-REDUCTION 168 3984.922484 3972.778681 REFLECTION 170 3984.827838 3972.139835 LO-REDUCTION 172 3981.087107 3972.139835 LO-REDUCTION 174 3979.721924 3972.139835 REFLECTION 176 3978.442787 3969.869510 LO-REDUCTION 178 3977.141676 3969.869510 LO-REDUCTION 180 3973.236883 3969.869510 LO-REDUCTION 182 3972.887320 3969.869510 LO-REDUCTION 184 3972.778681 3969.869510 LO-REDUCTION 186 3972.162420 3969.869510 LO-REDUCTION 188 3972.139835 3969.738442 LO-REDUCTION 190 3971.014551 3969.598279 LO-REDUCTION 192 3970.552728 3969.598279 LO-REDUCTION 194 3970.172259 3969.494199 LO-REDUCTION 196 3970.166388 3969.494199 REFLECTION 198 3969.944096 3969.327735 HI-REDUCTION 200 3969.869510 3969.327735 LO-REDUCTION 202 3969.738442 3969.327735 LO-REDUCTION 204 3969.690249 3969.327735 HI-REDUCTION 206 3969.684626 3969.327735 LO-REDUCTION 208 3969.598279 3969.327735 LO-REDUCTION 210 3969.576744 3969.327735 EXTENSION 212 3969.494199 3969.169699 LO-REDUCTION 214 3969.482330 3969.169699 LO-REDUCTION 216 3969.473135 3969.169699 LO-REDUCTION 218 3969.424575 3969.169699 LO-REDUCTION 220 3969.396673 3969.169699 LO-REDUCTION 222 3969.384957 3969.169699 LO-REDUCTION 224 3969.327735 3969.169699 LO-REDUCTION 226 3969.310119 3969.169699 LO-REDUCTION 228 3969.300273 3969.169699 REFLECTION 230 3969.264989 3969.160040 LO-REDUCTION 232 3969.242905 3969.160040 LO-REDUCTION 234 3969.238794 3969.160040 LO-REDUCTION 236 3969.238305 3969.160040 REFLECTION 238 3969.199832 3969.132315 LO-REDUCTION 240 3969.178685 3969.132315 LO-REDUCTION 242 3969.177331 3969.132315 LO-REDUCTION 244 3969.174441 3969.132315 LO-REDUCTION 246 3969.173431 3969.132315 EXTENSION 248 3969.169699 3969.116740 LO-REDUCTION 250 3969.160040 3969.116740 LO-REDUCTION 252 3969.155284 3969.116740 LO-REDUCTION 254 3969.154092 3969.116740 LO-REDUCTION 256 3969.153605 3969.116740 LO-REDUCTION 258 3969.143908 3969.116740 EXTENSION 260 3969.138580 3969.110359 EXTENSION 262 3969.132423 3969.081176 LO-REDUCTION 264 3969.132315 3969.081176 LO-REDUCTION 266 3969.127132 3969.081176 LO-REDUCTION 268 3969.122569 3969.081176 LO-REDUCTION 270 3969.117094 3969.081176 LO-REDUCTION 272 3969.116740 3969.081176 LO-REDUCTION 274 3969.110359 3969.081176 LO-REDUCTION 276 3969.109788 3969.081176 EXTENSION 278 3969.107293 3969.075655 EXTENSION 280 3969.101599 3969.065509 EXTENSION 282 3969.097489 3969.056101 EXTENSION 284 3969.093620 3969.048100 LO-REDUCTION 286 3969.091514 3969.048100 LO-REDUCTION 288 3969.084910 3969.048100 LO-REDUCTION 290 3969.081176 3969.048100 LO-REDUCTION 292 3969.075655 3969.048100 REFLECTION 294 3969.065509 3969.045559 REFLECTION 296 3969.058562 3969.040315 LO-REDUCTION 298 3969.056965 3969.040315 EXTENSION 300 3969.056101 3969.025399 LO-REDUCTION 302 3969.053230 3969.025399 LO-REDUCTION 304 3969.052521 3969.025399 LO-REDUCTION 306 3969.048100 3969.025399 LO-REDUCTION 308 3969.045559 3969.025399 EXTENSION 310 3969.043419 3969.012725 LO-REDUCTION 312 3969.043417 3969.012725 EXTENSION 314 3969.040315 3968.996212 LO-REDUCTION 316 3969.031801 3968.996212 LO-REDUCTION 318 3969.031158 3968.996212 EXTENSION 320 3969.030020 3968.981360 LO-REDUCTION 322 3969.025399 3968.981360 EXTENSION 324 3969.018170 3968.971291 EXTENSION 326 3969.012725 3968.942839 LO-REDUCTION 328 3969.000735 3968.942839 LO-REDUCTION 330 3968.998467 3968.942839 LO-REDUCTION 332 3968.996212 3968.942839 REFLECTION 334 3968.985469 3968.938479 LO-REDUCTION 336 3968.981360 3968.938479 EXTENSION 338 3968.971291 3968.911657 LO-REDUCTION 340 3968.962002 3968.911657 LO-REDUCTION 342 3968.954811 3968.911657 LO-REDUCTION 344 3968.946371 3968.911657 EXTENSION 346 3968.944387 3968.895247 LO-REDUCTION 348 3968.942839 3968.895247 LO-REDUCTION 350 3968.940802 3968.895247 LO-REDUCTION 352 3968.938479 3968.895247 LO-REDUCTION 354 3968.921683 3968.895247 LO-REDUCTION 356 3968.916961 3968.895247 LO-REDUCTION 358 3968.912733 3968.895247 EXTENSION 360 3968.911657 3968.884907 LO-REDUCTION 362 3968.905532 3968.884907 LO-REDUCTION 364 3968.904616 3968.884907 LO-REDUCTION 366 3968.902689 3968.884907 EXTENSION 368 3968.902499 3968.870968 LO-REDUCTION 370 3968.895942 3968.870968 EXTENSION 372 3968.895247 3968.859388 LO-REDUCTION 374 3968.894811 3968.859388 EXTENSION 376 3968.886787 3968.848170 LO-REDUCTION 378 3968.885289 3968.848170 LO-REDUCTION 380 3968.884907 3968.848170 EXTENSION 382 3968.874080 3968.831239 LO-REDUCTION 384 3968.873805 3968.831239 LO-REDUCTION 386 3968.870968 3968.831239 REFLECTION 388 3968.861955 3968.830692 REFLECTION 390 3968.859983 3968.829868 EXTENSION 392 3968.859388 3968.814537 LO-REDUCTION 394 3968.848170 3968.814537 LO-REDUCTION 396 3968.839517 3968.814537 LO-REDUCTION 398 3968.832979 3968.814537 LO-REDUCTION 400 3968.831239 3968.814537 LO-REDUCTION 402 3968.830692 3968.814338 LO-REDUCTION 404 3968.829868 3968.814082 LO-REDUCTION 406 3968.824158 3968.811722 LO-REDUCTION 408 3968.816435 3968.811722 REFLECTION 410 3968.815157 3968.811532 HI-REDUCTION 412 3968.815011 3968.811532 REFLECTION 414 3968.814537 3968.809563 HI-REDUCTION 416 3968.814338 3968.809563 LO-REDUCTION 418 3968.814082 3968.809563 REFLECTION 420 3968.812178 3968.808884 HI-REDUCTION 422 3968.811810 3968.808884 LO-REDUCTION 424 3968.811722 3968.808884 HI-REDUCTION 426 3968.811532 3968.808884 LO-REDUCTION 428 3968.811504 3968.808884 LO-REDUCTION 430 3968.811469 3968.808884 REFLECTION 432 3968.810538 3968.808549 LO-REDUCTION 434 3968.810436 3968.808549 REFLECTION 436 3968.810402 3968.808429 EXTENSION 438 3968.809669 3968.807327 LO-REDUCTION 440 3968.809563 3968.807327 LO-REDUCTION 442 3968.809143 3968.807327 LO-REDUCTION 444 3968.808898 3968.807327 EXTENSION 446 3968.808884 3968.806697 LO-REDUCTION 448 3968.808549 3968.806697 LO-REDUCTION 450 3968.808429 3968.806697 LO-REDUCTION 452 3968.807705 3968.806697 EXTENSION 454 3968.807662 3968.805726 LO-REDUCTION 456 3968.807484 3968.805726 LO-REDUCTION 458 3968.807327 3968.805726 LO-REDUCTION 460 3968.807094 3968.805726 LO-REDUCTION 462 3968.807024 3968.805726 LO-REDUCTION 464 3968.806900 3968.805726 LO-REDUCTION 466 3968.806714 3968.805726 LO-REDUCTION 468 3968.806697 3968.805726 EXTENSION 470 3968.806641 3968.805477 EXTENSION 472 3968.806396 3968.805260 EXTENSION 474 3968.806289 3968.804989 EXTENSION 476 3968.806206 3968.804304 LO-REDUCTION 478 3968.806191 3968.804304 LO-REDUCTION 480 3968.806061 3968.804304 REFLECTION 482 3968.805726 3968.804141 REFLECTION 484 3968.805477 3968.803997 LO-REDUCTION 486 3968.805260 3968.803997 LO-REDUCTION 488 3968.804989 3968.803997 EXTENSION 490 3968.804430 3968.802943 LO-REDUCTION 492 3968.804411 3968.802943 LO-REDUCTION 494 3968.804304 3968.802943 LO-REDUCTION 496 3968.804168 3968.802943 LO-REDUCTION 498 3968.804141 3968.802943 LO-REDUCTION 500 3968.804010 3968.802943 Exiting from Nelder Mead minimizer 502 function evaluations used > heart.msm Call: msm(formula = state ~ years, subject = PTNUM, data = heart, qmatrix = twoway4.q, death = 4, control = list(trace = 2, REPORT = 1)) Maximum likelihood estimates: Transition intensity matrix State 1 State 2 State 1 -0.1702 (-0.1901,-0.1524) 0.1277 (0.1112,0.1466) State 2 0.2244 (0.167,0.3016) -0.6062 (-0.7068,-0.5199) State 3 0 0.1312 (0.08003,0.2152) State 4 0 0 State 3 State 4 State 1 0 0.04253 (0.03414,0.05298) State 2 0.3406 (0.2714,0.4273) 0.0412 (0.01193,0.1423) State 3 -0.4361 (-0.5517,-0.3447) 0.3049 (0.2368,0.3925) State 4 0 0 -2 * log-likelihood: 3968.803 > qmatrix.msm(heart.msm) State 1 State 2 State 1 -0.1702 (-0.1901,-0.1524) 0.1277 (0.1112,0.1466) State 2 0.2244 (0.167,0.3016) -0.6062 (-0.7068,-0.5199) State 3 0 0.1312 (0.08003,0.2152) State 4 0 0 State 3 State 4 State 1 0 0.04253 (0.03414,0.05298) State 2 0.3406 (0.2714,0.4273) 0.0412 (0.01193,0.1423) State 3 -0.4361 (-0.5517,-0.3447) 0.3049 (0.2368,0.3925) State 4 0 0 > pmatrix.msm(heart.msm, t=10) State 1 State 2 State 3 State 4 State 1 0.30959690 0.09780067 0.08775948 0.5048430 State 2 0.17187999 0.06588634 0.07810046 0.6841332 State 3 0.05943821 0.03009829 0.04705873 0.8634048 State 4 0.00000000 0.00000000 0.00000000 1.0000000 > sojourn.msm(heart.msm) estimates SE L U State 1 5.874810 0.3310261 5.260554 6.560791 State 2 1.649685 0.1292902 1.414784 1.923587 State 3 2.292950 0.2750939 1.812478 2.900791 > > > > cleanEx(); ..nameEx <- "pexp" > > ### * pexp > > flush(stderr()); flush(stdout()) > > ### Name: pexp > ### Title: Exponential distribution with piecewise-constant rate > ### Aliases: pexp dpexp ppexp qpexp rpexp > > > ### ** Examples > > x <- seq(0.1, 50, by=0.1) > rate <- c(0.1, 0.2, 0.05, 0.3) > t <- c(0, 10, 20, 30) > plot(x, dexp(x, 0.1), type="l") ## standard exponential distribution > lines(x, dpexp(x, rate, t), type="l", lty=2) ## distribution with piecewise constant rate > plot(x, pexp(x, 0.1), type="l") ## standard exponential distribution > lines(x, ppexp(x, rate, t), type="l", lty=2) ## distribution with piecewise constant rate > > > > cleanEx(); ..nameEx <- "pmatrix.piecewise.msm" > > ### * pmatrix.piecewise.msm > > flush(stderr()); flush(stdout()) > > ### Name: pmatrix.piecewise.msm > ### Title: Transition probability matrix for processes with > ### piecewise-constant intensities > ### Aliases: pmatrix.piecewise.msm > > > ### ** Examples > > ## Not run: > ##D ## In a clinical study, suppose patients are given a placebo in the > ##D ## first 5 weeks, then they begin treatment 1 at 5 weeks, and > ##D ## a combination of treatments 1 and 2 from 10 weeks. > ##D ## Suppose a multi-state model x has been fitted for the patients' > ##D ## progress, with treat1 and treat2 as time dependent covariates. > ##D > ##D ## Cut points for when treatment covariate changes > ##D times <- c(0, 5, 10) > ##D > ##D ## Indicators for which treatments are active at the three cut points > ##D covariates <- list( list (treat1=0, treat2=0), list(treat1=1, treat2=0), > ##D list(treat1=1, treat2=1) ) > ##D > ##D ## Calculate transition probabilities from the start of the study to 15 weeks > ##D pmatrix.piecewise.msm(x, 0, 15, times, covariates) > ## End(Not run) > > > > cleanEx(); ..nameEx <- "psor" > > ### * psor > > flush(stderr()); flush(stdout()) > > ### Name: psor > ### Title: Psoriatic arthritis data > ### Aliases: psor > ### Keywords: datasets > > ### ** Examples > > ## Four-state progression-only model with high effusion and low > ## sedimentation rate as covariates on the progression rates. High > ## effusion is assumed to have the same effect on the 1-2, 2-3, and 3-4 > ## progression rates, while low sedimentation rate has the same effect > ## on the 1-2 and 2-3 intensities, but a different effect on the 3-4. > > data(psor) > psor.q <- rbind(c(0,0.1,0,0),c(0,0,0.1,0),c(0,0,0,0.1),c(0,0,0,0)) > psor.msm <- msm(state ~ months, subject=ptnum, data=psor, + qmatrix = psor.q, covariates = ~ollwsdrt+hieffusn, + constraint = list(hieffusn=c(1,1,1),ollwsdrt=c(1,1,2)), + fixedpars=FALSE, control = list(REPORT=1,trace=2), method="BFGS") Warning in msm.form.data(formula, subject, obstype, covariates, data, hcovariates, : 86 records dropped due to missing values initial value 1184.216999 iter 2 value 1127.362630 iter 3 value 1122.598523 iter 4 value 1121.486172 iter 5 value 1120.635122 iter 6 value 1119.689026 iter 7 value 1116.738821 iter 8 value 1116.597013 iter 9 value 1114.994129 iter 10 value 1114.899957 iter 11 value 1114.899464 iter 11 value 1114.899461 iter 11 value 1114.899461 final value 1114.899461 converged > qmatrix.msm(psor.msm) State 1 State 2 State 1 -0.09539 (-0.1209,-0.07524) 0.09539 (0.07524,0.1209) State 2 0 -0.1634 (-0.2065,-0.1293) State 3 0 0 State 4 0 0 State 3 State 4 State 1 0 0 State 2 0.1634 (0.1293,0.2065) 0 State 3 -0.2552 (-0.3413,-0.1909) 0.2552 (0.1909,0.3413) State 4 0 0 > sojourn.msm(psor.msm) estimates SE L U State 1 10.483473 1.2694592 8.268599 13.291636 State 2 6.121074 0.7316112 4.842718 7.736884 State 3 3.918045 0.5810499 2.929782 5.239664 > hazard.msm(psor.msm) $ollwsdrt HR L U State 1 - State 2 0.5651906 0.3853472 0.8289677 State 2 - State 3 0.5651906 0.3853472 0.8289677 State 3 - State 4 1.6407602 0.8154540 3.3013439 $hieffusn HR L U State 1 - State 2 1.645954 1.148299 2.359284 State 2 - State 3 1.645954 1.148299 2.359284 State 3 - State 4 1.645954 1.148299 2.359284 > > > > cleanEx(); ..nameEx <- "sim.msm" > > ### * sim.msm > > flush(stderr()); flush(stdout()) > > ### Name: sim.msm > ### Title: Simulate one individual trajectory from a continuous-time Markov > ### model > ### Aliases: sim.msm > ### Keywords: models > > ### ** Examples > > > qmatrix <- rbind( + c(-0.2, 0.1, 0.1 ), + c(0.5, -0.6, 0.1 ), + c(0, 0, 0) + ) > sim.msm(qmatrix, 30) $states [1] 1 3 $times [1] 0.000000 3.775909 $qmatrix [,1] [,2] [,3] [1,] -0.2 0.1 0.1 [2,] 0.5 -0.6 0.1 [3,] 0.0 0.0 0.0 > > > > cleanEx(); ..nameEx <- "simmulti.msm" > > ### * simmulti.msm > > flush(stderr()); flush(stdout()) > > ### Name: simmulti.msm > ### Title: Simulate multiple trajectories from a multi-state Markov model > ### with arbitrary observation times > ### Aliases: simmulti.msm > ### Keywords: models > > ### ** Examples > > ### Simulate 100 individuals with common observation times > sim.df <- data.frame(subject = rep(1:100, rep(13,100)), time = rep(seq(0, 24, 2), 100)) > qmatrix <- rbind(c(-0.11, 0.1, 0.01 ), + c(0.05, -0.15, 0.1 ), + c(0.02, 0.07, -0.09)) > simmulti.msm(sim.df, qmatrix) subject time state 1 1 0 1 2 1 2 1 3 1 4 1 4 1 6 1 5 1 8 1 6 1 10 1 7 1 12 1 8 1 14 1 9 1 16 1 10 1 18 1 11 1 20 1 12 1 22 1 13 1 24 1 14 2 0 1 15 2 2 1 16 2 4 2 17 2 6 2 18 2 8 2 19 2 10 2 20 2 12 2 21 2 14 2 22 2 16 2 23 2 18 3 24 2 20 3 25 2 22 2 26 2 24 2 27 3 0 1 28 3 2 1 29 3 4 3 30 3 6 3 31 3 8 3 32 3 10 3 33 3 12 3 34 3 14 3 35 3 16 3 36 3 18 2 37 3 20 3 38 3 22 3 39 3 24 3 40 4 0 1 41 4 2 1 42 4 4 1 43 4 6 1 44 4 8 1 45 4 10 1 46 4 12 1 47 4 14 1 48 4 16 1 49 4 18 1 50 4 20 1 51 4 22 1 52 4 24 1 53 5 0 1 54 5 2 1 55 5 4 1 56 5 6 1 57 5 8 1 58 5 10 2 59 5 12 2 60 5 14 1 61 5 16 1 62 5 18 1 63 5 20 2 64 5 22 2 65 5 24 3 66 6 0 1 67 6 2 1 68 6 4 1 69 6 6 1 70 6 8 1 71 6 10 1 72 6 12 1 73 6 14 1 74 6 16 1 75 6 18 1 76 6 20 1 77 6 22 1 78 6 24 1 79 7 0 1 80 7 2 1 81 7 4 1 82 7 6 2 83 7 8 2 84 7 10 2 85 7 12 3 86 7 14 3 87 7 16 3 88 7 18 3 89 7 20 3 90 7 22 3 91 7 24 3 92 8 0 1 93 8 2 1 94 8 4 1 95 8 6 1 96 8 8 1 97 8 10 2 98 8 12 3 99 8 14 2 100 8 16 3 101 8 18 3 102 8 20 3 103 8 22 3 104 8 24 1 105 9 0 1 106 9 2 1 107 9 4 1 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68 22 3 884 68 24 3 885 69 0 1 886 69 2 1 887 69 4 1 888 69 6 1 889 69 8 1 890 69 10 1 891 69 12 1 892 69 14 1 893 69 16 1 894 69 18 1 895 69 20 1 896 69 22 1 897 69 24 1 898 70 0 1 899 70 2 1 900 70 4 1 901 70 6 1 902 70 8 1 903 70 10 1 904 70 12 1 905 70 14 1 906 70 16 1 907 70 18 1 908 70 20 1 909 70 22 2 910 70 24 2 911 71 0 1 912 71 2 1 913 71 4 1 914 71 6 1 915 71 8 1 916 71 10 2 917 71 12 2 918 71 14 1 919 71 16 2 920 71 18 2 921 71 20 2 922 71 22 2 923 71 24 2 924 72 0 1 925 72 2 3 926 72 4 3 927 72 6 2 928 72 8 2 929 72 10 2 930 72 12 3 931 72 14 3 932 72 16 3 933 72 18 3 934 72 20 2 935 72 22 3 936 72 24 3 937 73 0 1 938 73 2 1 939 73 4 2 940 73 6 3 941 73 8 3 942 73 10 3 943 73 12 3 944 73 14 3 945 73 16 3 946 73 18 3 947 73 20 3 948 73 22 3 949 73 24 3 950 74 0 1 951 74 2 2 952 74 4 2 953 74 6 2 954 74 8 2 955 74 10 2 956 74 12 2 957 74 14 2 958 74 16 2 959 74 18 2 960 74 20 2 961 74 22 2 962 74 24 1 963 75 0 1 964 75 2 1 965 75 4 1 966 75 6 2 967 75 8 2 968 75 10 2 969 75 12 2 970 75 14 2 971 75 16 2 972 75 18 2 973 75 20 2 974 75 22 2 975 75 24 2 976 76 0 1 977 76 2 2 978 76 4 2 979 76 6 3 980 76 8 3 981 76 10 3 982 76 12 3 983 76 14 3 984 76 16 3 985 76 18 3 986 76 20 2 987 76 22 2 988 76 24 3 989 77 0 1 990 77 2 2 991 77 4 2 992 77 6 2 993 77 8 2 994 77 10 2 995 77 12 2 996 77 14 3 997 77 16 3 998 77 18 3 999 77 20 3 1000 77 22 3 1001 77 24 3 1002 78 0 1 1003 78 2 1 1004 78 4 1 1005 78 6 1 1006 78 8 1 1007 78 10 1 1008 78 12 1 1009 78 14 1 1010 78 16 2 1011 78 18 2 1012 78 20 2 1013 78 22 2 1014 78 24 2 1015 79 0 1 1016 79 2 1 1017 79 4 1 1018 79 6 1 1019 79 8 1 1020 79 10 1 1021 79 12 1 1022 79 14 1 1023 79 16 2 1024 79 18 2 1025 79 20 1 1026 79 22 1 1027 79 24 2 1028 80 0 1 1029 80 2 2 1030 80 4 3 1031 80 6 2 1032 80 8 2 1033 80 10 3 1034 80 12 3 1035 80 14 2 1036 80 16 2 1037 80 18 2 1038 80 20 2 1039 80 22 2 1040 80 24 2 1041 81 0 1 1042 81 2 2 1043 81 4 2 1044 81 6 1 1045 81 8 1 1046 81 10 1 1047 81 12 1 1048 81 14 1 1049 81 16 3 1050 81 18 3 1051 81 20 3 1052 81 22 3 1053 81 24 3 1054 82 0 1 1055 82 2 1 1056 82 4 1 1057 82 6 1 1058 82 8 1 1059 82 10 1 1060 82 12 2 1061 82 14 2 1062 82 16 2 1063 82 18 1 1064 82 20 1 1065 82 22 1 1066 82 24 1 1067 83 0 1 1068 83 2 2 1069 83 4 2 1070 83 6 2 1071 83 8 2 1072 83 10 3 1073 83 12 3 1074 83 14 3 1075 83 16 2 1076 83 18 2 1077 83 20 2 1078 83 22 1 1079 83 24 1 1080 84 0 1 1081 84 2 1 1082 84 4 1 1083 84 6 1 1084 84 8 2 1085 84 10 2 1086 84 12 2 1087 84 14 2 1088 84 16 3 1089 84 18 3 1090 84 20 3 1091 84 22 3 1092 84 24 3 1093 85 0 1 1094 85 2 1 1095 85 4 2 1096 85 6 2 1097 85 8 2 1098 85 10 2 1099 85 12 2 1100 85 14 2 1101 85 16 3 1102 85 18 3 1103 85 20 3 1104 85 22 3 1105 85 24 3 1106 86 0 1 1107 86 2 1 1108 86 4 2 1109 86 6 1 1110 86 8 1 1111 86 10 1 1112 86 12 1 1113 86 14 1 1114 86 16 1 1115 86 18 2 1116 86 20 2 1117 86 22 2 1118 86 24 2 1119 87 0 1 1120 87 2 1 1121 87 4 1 1122 87 6 1 1123 87 8 1 1124 87 10 1 1125 87 12 1 1126 87 14 1 1127 87 16 1 1128 87 18 1 1129 87 20 1 1130 87 22 1 1131 87 24 1 1132 88 0 1 1133 88 2 1 1134 88 4 1 1135 88 6 1 1136 88 8 1 1137 88 10 1 1138 88 12 2 1139 88 14 1 1140 88 16 1 1141 88 18 1 1142 88 20 1 1143 88 22 1 1144 88 24 1 1145 89 0 1 1146 89 2 2 1147 89 4 2 1148 89 6 2 1149 89 8 3 1150 89 10 3 1151 89 12 1 1152 89 14 1 1153 89 16 1 1154 89 18 1 1155 89 20 1 1156 89 22 3 1157 89 24 3 1158 90 0 1 1159 90 2 1 1160 90 4 1 1161 90 6 2 1162 90 8 3 1163 90 10 3 1164 90 12 3 1165 90 14 3 1166 90 16 3 1167 90 18 3 1168 90 20 3 1169 90 22 3 1170 90 24 3 1171 91 0 1 1172 91 2 1 1173 91 4 1 1174 91 6 1 1175 91 8 1 1176 91 10 1 1177 91 12 1 1178 91 14 1 1179 91 16 1 1180 91 18 1 1181 91 20 1 1182 91 22 1 1183 91 24 2 1184 92 0 1 1185 92 2 2 1186 92 4 2 1187 92 6 2 1188 92 8 2 1189 92 10 2 1190 92 12 2 1191 92 14 2 1192 92 16 2 1193 92 18 2 1194 92 20 2 1195 92 22 2 1196 92 24 2 1197 93 0 1 1198 93 2 1 1199 93 4 1 1200 93 6 1 1201 93 8 2 1202 93 10 2 1203 93 12 3 1204 93 14 3 1205 93 16 3 1206 93 18 3 1207 93 20 3 1208 93 22 3 1209 93 24 3 1210 94 0 1 1211 94 2 1 1212 94 4 1 1213 94 6 1 1214 94 8 1 1215 94 10 1 1216 94 12 1 1217 94 14 1 1218 94 16 1 1219 94 18 1 1220 94 20 1 1221 94 22 1 1222 94 24 1 1223 95 0 1 1224 95 2 1 1225 95 4 1 1226 95 6 1 1227 95 8 1 1228 95 10 1 1229 95 12 1 1230 95 14 1 1231 95 16 1 1232 95 18 1 1233 95 20 1 1234 95 22 1 1235 95 24 1 1236 96 0 1 1237 96 2 1 1238 96 4 1 1239 96 6 1 1240 96 8 1 1241 96 10 1 1242 96 12 1 1243 96 14 1 1244 96 16 2 1245 96 18 1 1246 96 20 1 1247 96 22 1 1248 96 24 1 1249 97 0 1 1250 97 2 2 1251 97 4 2 1252 97 6 3 1253 97 8 3 1254 97 10 3 1255 97 12 3 1256 97 14 3 1257 97 16 3 1258 97 18 3 1259 97 20 2 1260 97 22 2 1261 97 24 1 1262 98 0 1 1263 98 2 1 1264 98 4 1 1265 98 6 1 1266 98 8 1 1267 98 10 3 1268 98 12 3 1269 98 14 3 1270 98 16 3 1271 98 18 3 1272 98 20 3 1273 98 22 3 1274 98 24 2 1275 99 0 1 1276 99 2 1 1277 99 4 1 1278 99 6 2 1279 99 8 2 1280 99 10 2 1281 99 12 2 1282 99 14 2 1283 99 16 2 1284 99 18 3 1285 99 20 1 1286 99 22 1 1287 99 24 1 1288 100 0 1 1289 100 2 1 1290 100 4 1 1291 100 6 1 1292 100 8 1 1293 100 10 1 1294 100 12 1 1295 100 14 1 1296 100 16 1 1297 100 18 1 1298 100 20 1 1299 100 22 1 1300 100 24 1 > > > > cleanEx(); ..nameEx <- "statetable.msm" > > ### * statetable.msm > > flush(stderr()); flush(stdout()) > > ### Name: statetable.msm > ### Title: Table of transitions > ### Aliases: statetable.msm > > > ### ** Examples > > ## Heart transplant data > data(heart) > > ## 148 deaths from state 1, 48 from state 2 and 55 from state 3. > statetable.msm(state, PTNUM, data=heart) to from 1 2 3 4 1 1367 204 44 148 2 46 134 54 48 3 4 13 107 55 > > > > > cleanEx(); ..nameEx <- "tnorm" > > ### * tnorm > > flush(stderr()); flush(stdout()) > > ### Name: tnorm > ### Title: Truncated Normal distribution > ### Aliases: tnorm dtnorm ptnorm qtnorm rtnorm > > > ### ** Examples > > x <- seq(50, 90, by=1) > plot(x, dnorm(x, 70, 10), type="l", ylim=c(0,0.06)) ## standard Normal distribution > lines(x, dtnorm(x, 70, 10, 60, 80), type="l") ## truncated Normal distribution > > > > ### *